Optimization of PET Image Reconstruction for Lesion Detection Abstract PET is a molecular imaging modality widely used in oncology studies due to its high sensitivity and the potential of early diagnosis. For neuroendocrine tumors (NETs), 68Ga-DOTATATE PET has been recently used in clinical routine for imaging NETs in adult and pediatric patients since 2016. It plays an important role in the diagnosis and staging of NETs. However, compared to 18F-FDG PET, the image quality of 68Ga-DOTATATE PET is lower due to much larger positron range, shorter half-life, and lower dose administration limited by generator capacity. All of these compromises the lesion detectability of 68Ga-DOTATATE PET, especially for small lesions, and can potentially lead to inaccurate NET diagnosis. As 68Ga-DOTATATE PET is increasingly used in clinics, there is an urgent and unmet need to further optimize 68Ga-DOTATATE PET/CT imaging for NET detection. Recently, data-driven methods have been developed for PET image denoising, where the PET system model is not considered. As the tumor-to-background ratio of 68Ga-DOTATATE PET is greater than 18F-FDG PET, the lesion recovery of 68Ga-DOTATATE PET can be hugely influenced by the smoothing effects as well as potential mismatches between training and testing datasets. In this study, we propose a novel data- informed and lesion detection-driven image reconstruction framework. The PET system model, image denoising module, and lesion-detection module will all be included in this reconstruction framework. The two specific aims of this exploratory proposal are (1) to develop a lesion detection-driven PET image reconstruction framework and validate it based on comprehensive computer simulations, (2) to apply the proposed reconstruction framework to existing clinical 68Ga-DOTATATE PET/CT datasets and test it based on various figure-of-merits. We expect that the integrated outcome of the specific aims will be a novel and robust image reconstruction framework to better recover lesions in a 68Ga- DOTATATE PET scan, which is essential for NET managements.